Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System
This paper introduces an energy management strategy for an off-grid hybrid energy system. The hybrid system consists of a photovoltaic (PV) module, a LiFePO4 battery pack coupled with a Battery Management System (BMS), a hybrid solar inverter, and a load management control unit. A Long Short-Term Me...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/19/6427 |
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| author | Tareq Alnejaili Sami Labdai Larbi Chrifi-Alaoui |
| author_facet | Tareq Alnejaili Sami Labdai Larbi Chrifi-Alaoui |
| author_sort | Tareq Alnejaili |
| collection | DOAJ |
| description | This paper introduces an energy management strategy for an off-grid hybrid energy system. The hybrid system consists of a photovoltaic (PV) module, a LiFePO4 battery pack coupled with a Battery Management System (BMS), a hybrid solar inverter, and a load management control unit. A Long Short-Term Memory network (LSTM)-based forecasting strategy is implemented to predict the available PV and battery power. The learning data are extracted from an African country with a tropical climate, which is very suitable for PV power applications. Using LSTM as a prediction method significantly increases the efficiency of the forecasting. The main objective of the proposed strategy is to control the different loads according to the forecasted energy availability of the system and the forecasted battery state of charge (SOC). The proposed management algorithm and the system are tested using Matlab/Simulink software. A comparative study demonstrates that the reduction in the energy deficit of the system is approximately 53% compared to the system without load management. In addition to this, the reliability of the system is improved as the loss of power supply probability (LPSP) decreases from 5% to 3%. |
| first_indexed | 2024-03-10T06:51:54Z |
| format | Article |
| id | doaj.art-331d24dab15644d384ab97426b448820 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T06:51:54Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-331d24dab15644d384ab97426b4488202023-11-22T16:45:56ZengMDPI AGSensors1424-82202021-09-012119642710.3390/s21196427Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy SystemTareq Alnejaili0Sami Labdai1Larbi Chrifi-Alaoui2Innovative Technologies Laboratory (LTI UR 3899), University of Picardie Jules Verne, 13 av. F. Mitterrand, 02880 Cuffies, FranceInnovative Technologies Laboratory (LTI UR 3899), University of Picardie Jules Verne, 13 av. F. Mitterrand, 02880 Cuffies, FranceInnovative Technologies Laboratory (LTI UR 3899), University of Picardie Jules Verne, 13 av. F. Mitterrand, 02880 Cuffies, FranceThis paper introduces an energy management strategy for an off-grid hybrid energy system. The hybrid system consists of a photovoltaic (PV) module, a LiFePO4 battery pack coupled with a Battery Management System (BMS), a hybrid solar inverter, and a load management control unit. A Long Short-Term Memory network (LSTM)-based forecasting strategy is implemented to predict the available PV and battery power. The learning data are extracted from an African country with a tropical climate, which is very suitable for PV power applications. Using LSTM as a prediction method significantly increases the efficiency of the forecasting. The main objective of the proposed strategy is to control the different loads according to the forecasted energy availability of the system and the forecasted battery state of charge (SOC). The proposed management algorithm and the system are tested using Matlab/Simulink software. A comparative study demonstrates that the reduction in the energy deficit of the system is approximately 53% compared to the system without load management. In addition to this, the reliability of the system is improved as the loss of power supply probability (LPSP) decreases from 5% to 3%.https://www.mdpi.com/1424-8220/21/19/6427energy managementforecastingrenewable energyPV systemload side managementhybrid energy system |
| spellingShingle | Tareq Alnejaili Sami Labdai Larbi Chrifi-Alaoui Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System Sensors energy management forecasting renewable energy PV system load side management hybrid energy system |
| title | Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System |
| title_full | Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System |
| title_fullStr | Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System |
| title_full_unstemmed | Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System |
| title_short | Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System |
| title_sort | predictive management algorithm for controlling pv battery off grid energy system |
| topic | energy management forecasting renewable energy PV system load side management hybrid energy system |
| url | https://www.mdpi.com/1424-8220/21/19/6427 |
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